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Abstract With the rapid development of nuclear energy, problems with uranium supply chain and nuclear waste accumulation have motivated researchers to improve uranium separation methods. Here we show a paradigm for such goal based on the in-situ formation of π-f conjugated two-dimensional uranium-organic framework. After screening five π-conjugated organic ligands, we find that 1,3,5-triformylphloroglucinol would be the best one to construct uranium-organic framework, thus resulting in 100% uranium removal from both high and low concentration with the residual concentration far below the WHO drinking water standard (15 ppb), and 97% uranium capture from natural seawater (3.3 ppb) with a record uptake efficiency of 0.64 mg·g−1·d−1. We also find that 1,3,5-triformylphloroglucinol can overcome the ion-interference issue such as the presence of massive interference ions or a 21-ions mixed solution. Our finds confirm the superiority of our separation approach over established ones, and will provide a fundamental molecule design for separation upon metal-organic framework chemistry.

A multi-criteria decision-making (MCDM) method with single-valued neutrosophic information is developed based on the Partitioned Heronian Mean (PHM) operator and the Shapley fuzzy measure, which recognizes correlation among the selection criteria. Motivated by the PHM operator and Shapley fuzzy measure, two new aggregation operators, namely the single-valued neutrosophic PHM operator and the weighted single-valued neutrosophic Shapley PHM operator, are defined, and their corresponding properties and some special cases are investigated. An MCDM model is applied to solve the single-valued neutrosophic problem where weight information is not completely known. An example is provided to validate the proposed method.

Abstract Designing efficient catalyst for the oxygen evolution reaction (OER) is of importance for energy conversion devices. The anionic redox allows formation of O-O bonds and offers higher OER activity than the conventional metal sites. Here, we successfully prepare LiNiO2 with a dominant 3d 8 L configuration (L is a hole at O 2p) under high oxygen pressure, and achieve a double ligand holes 3d 8 L 2 under OER since one electron removal occurs at O 2p orbitals for NiIII oxides. LiNiO2 exhibits super-efficient OER activity among LiMO2, RMO3 (M = transition metal, R = rare earth) and other unary 3d catalysts. Multiple in situ/operando spectroscopies reveal NiIII→NiIV transition together with Li-removal during OER. Our theory indicates that NiIV (3d 8 L 2) leads to direct O-O coupling between lattice oxygen and *O intermediates accelerating the OER activity. These findings highlight a new way to design the lattice oxygen redox with enough ligand holes created in OER process.

Membranes with fast and selective ion transport are essential for electrochemical processes. Here the authors provide mechanistic insights into the structures of metal-ion coordinated polybenzimidazole membranes and the preferential K+ transport, and their application in an alkaline zinc-iron flow battery.

Abstract MXene‐supported noble metal alloy catalysts exhibit remarkable electrocatalytic activity in various applications. However, there is no facile one‐step method for synthesizing these catalysts, because the synthesis of MXenes requires a strongly oxidizing environment and the preparation of platinum nanoalloys requires a strongly reducing environment and high temperatures. Hence, achieving coupling in one step is extremely challenging. In this paper, a straightforward one‐step molten salt method for preparing MXene‐supported platinum nanoalloy catalysts is proposed. The molten salt acts as the reaction medium to dissolve the transition metals and platinum ions at high temperatures. Transition metal ions oxidize the A‐site element from its MAX precursor at high temperatures, and the resulting transition metals further reduce platinum ions to form alloys. By coupling Al oxidation and platinum ion reduction using a molten salt solvent, this method directly converts Ti3AlC2 to a Pt‐M@Ti3C2Tx catalyst (where M denotes the transition metal). It further offers the possibility of extending the Pt‐M phase to binary, ternary, or quaternary platinum‐containing nanoalloys and converting the Al‐containing MAX phase to Ti2AlC and Ti3AlCN. Due to the strong interfacial interaction, the as‐prepared Pt‐Co@Ti3C2Tx is superior to commercial Pt/C (20 wt.%) in the hydrogen evolution reaction.

Cisplatin is the most widely used anticancer chemotherapy drug at present. However, its severe nephrotoxic side effects limit the clinical application. This study aimed to investigate the potential protective mechanism of arabinogalactan (AG) on acute kidney injury (AKI) induced by cisplatin in mice. Firstly, 105 potential targets of AG were predicted by using the SwissTargetPrediction database, 25,183 kidney disease targets were obtained by searching 5 disease databases, and 100 cross-target targets were obtained by drawing a Venn diagram. We constructed the “AG-Key target” interaction network and PPI protein interaction network of cross-targets and screened out 21 core targets. Through the GO and KEGG enrichment analysis of the core targets, we can found that the core targets involved a total of 1254 biological functions and 56 signaling pathways, including the apoptosis signaling pathway. By establishing an animal model in vivo, we found that pretreatment with AG at doses of 200 and 400 mg/kg could decrease the kidney index, such as serum creatinine (CRE), and blood urea nitrogen (BUN) levels induced by cisplatin, and reduce the kidney histopathology injury. Importantly, AG could inhibit the formation of metaldehyde (MDA) and 4-hydroxynonenal (4-HNE) in the kidney tissues and reduce the oxidative stress injury induced by cisplatin. Meanwhile, AG also restored the expression of anti-apoptotic proteins Bcl-2 and Bcl-xL and decreased the expression of pro-apoptotic proteins Bax and Bad in cisplatin-induced renal tissues. Finally, AG could further reduce cisplatin-induced kidney cell apoptosis.

Solid oxide electrolysis cells (SOECs) have great application prospects because of their excellent performance, but the long-term applications of the stacks are restricted by the structural degradation under the high-temperature conditions. Therefore, an SOEC degradation model is developed and embedded in a process model of the high-temperature steam electrolysis (HTSE) system to investigate the influence of the stack degradation at the system level. The sensitivity analysis and optimization were carried out to study the influence factors of the stack degradation and system hydrogen production efficiency and search for the optimal operating conditions to improve the hydrogen production efficiency and mitigate the stack degradation. The analysis results show that the high temperature and large current density can accelerate the stack degradation but improve the hydrogen production efficiency, while the high temperature gradually becomes unfavorable in the late stage. The low air-to-fuel feed ratio is beneficial to both the degradation rate and hydrogen production efficiency. The results show that the optimization method can improve the hydrogen production efficiency and inhibit the stack degradation effectively. Moreover, part of the hydrogen production efficiency has to be sacrificed in order to obtain a lower stack degradation rate.

High efficiency, raw material availability, and compatibility with downstream systems will enable the Solid Oxide Electrolysis Cell (SOEC) to play an important role in the future energy transition. However, the SOEC stack’s performance should be improved further by utilizing a novel flow-field design, and the channel shape is a key factor for enhancing gas transportation. To investigate the main effects of the novel channel design with fewer calculations, we assumed ideal gas laminar flows in the cathode channel. Furthermore, the cathode support layer thickness and electrical contact resistance are ignored. The conventional channel flow is validated first with mesh independence, and then the performance difference between the conventional and novel designs is analyzed using COMSOL Multiphysics. The process parameters such as velocity, pressure, current density, and mole concentration are compared between the conventional and novel designs, demonstrating that the novel design significantly improves electrolysis efficiency. Furthermore, it directly increased the concentration of product hydrogen in the novel channel. In addition to enhancing convection and diffusion of reaction gases in neighboring channels, the simple structure makes it easy to manufacture, which is advantageous for accelerating commercial use of the novel design.

The online reviews provided by patients contain many aspects of patient satisfaction (PS). An accurate understanding of PS can help hospitals and doctors quickly find the direction of medical service improvement and help patients select appropriate doctors. However, online reviews are texts in which patients show true feelings without constraints. Therefore, identifying, measuring, and representing PS are difficult. To solve these problems, we propose the online review analysis-based multi-criteria decision-making (MCDM) method. First, an aspect extraction method integrating dependency parsing and attention-based aspect extraction (ABAE) is proposed, and nine criteria for PS evaluation are extracted from the online reviews of the Haodf website. Second, a sentiment analysis method based on multiple dictionaries and dependency relations is developed to measure PS under each criterion in reviews. Then, an MCDM method based on a probabilistic linguistic term set representing PS is used to assess PS when considering patients’ loss aversion. Finally, the proposed method is verified in the evaluation of lung cancer patients’ satisfaction with doctors. The results show that our extracted criteria have higher coherence and accuracy compared to those extracted by other aspect extraction methods, and the proposed online review analysis-based MCDM method outperforms state-of-the-art methods in PS identification, measurement, and representation. [ABSTRACT FROM AUTHOR]

ABSTRACT: The South Yellow Sea Basin is a large sedimentary basin superimposed by the Mesozoic-Paleozoic marine sedimentary basin and the Mesozoic-Cenozoic terrestrial sedimentary basin, where no oil and gas fields have been discovered after exploration for 58 years. After the failure of oil and gas exploration in terrestrial basins, the exploration target of the South Yellow Sea Basin turned to the marine Mesozoic-Paleozoic strata. After more than ten years’ investigation and research, a lot of achievements have been obtained. The latest exploration obtained effective seismic reflection data of deep marine facies by the application of seismic exploration technology characterized by high coverage, abundant low-frequency components and strong energy source for the deep South Yellow Sea Basin. In addition, some wells drilled the Middle-Upper Paleozoic strata, with obvious oil and gas shows discovered in some horizons. The recent petroleum geological research on the South Yellow Sea Basin shows that the structure zoning of the marine residual basin has been redetermined, the basin structure has been defined, and 3 seismic reflection marker layers are traceable and correlatable in the residual thick Middle-Paleozoic strata below the continental Meso-Cenozoic strata in the South Yellow Sea Basin. Based on these, the seismic sequence of the marine sedimentary strata was established. According to the avaliable oil and gas exploration and research, the marine Mesozoic-Paleozoic oil and gas prospects of the South Yellow Sea were predicted as follows. (1) The South Yellow Sea Basin has the same sedimentary formation and evolution history during the sedimentary period of the Middle-Paleozoic marine basin with the Sichuan Basin. (2) There are 3 regional high-quality source rocks. (3) The carbonate and clastic reservoirs are developed in the Mesozoic-Paleozoic strata. (4) The three source-reservoir-cap assemblages are relatively intact. (5) The Laoshan Uplift is a prospect area for the Lower Paleozoic oil and gas, and the Wunansha Uplift is one for the marine Upper Paleozoic oil and gas. (6) The Gaoshi stable zone in the Laoshan Uplift is a favorable zone. (7) The marine Mesozoic-Paleozoic strata in the South Yellow Sea Basin has the geological conditions required to form large oil and gas fields, with remarkable oil and gas resources prospect. An urgent problem to be addressed now within the South Yellow Sea Basin is to drill parametric wells for the Lower Paleozoic strata as the target, to establish the complete stratigraphic sequence since the Paleozoic period, to obtain resource evaluation parameters, and to realize the strategic discovery and achieve breakthrough in oil and gas exploration understanding.©2019 China Geology Editorial Office.

Over the years, research on fuzzy clustering algorithms has attracted the attention of many researchers, and they have been applied to various areas, such as image segmentation and data clustering. Various fuzzy clustering algorithms have been put forward based on the initial Fuzzy C-Means clustering (FCM) with Euclidean distance. However, the existing fuzzy clustering approaches ignore two problems. Firstly, clustering algorithms based on Euclidean distance have a high error rate, and are more sensitive to noise and outliers. Secondly, the parameters of the fuzzy clustering algorithms are hard to determine. In practice, they are often determined by the user’s experience, which results in poor performance of the clustering algorithm. Therefore, considering the above deficiencies, this paper proposes a novel fuzzy clustering algorithm by combining the Gaussian kernel function and Grey Wolf Optimizer (GWO), called Kernel-based Picture Fuzzy C-Means clustering with Grey Wolf Optimizer (KPFCM-GWO). In KPFCM-GWO, the Gaussian kernel function is used as a symmetrical measure of distance between data points and cluster centers, and the GWO is utilized to determine the parameter values of PFCM. To verify the validity of KPFCM-GWO, a comparative study was conducted. The experimental results indicate that KPFCM-GWO outperforms other clustering methods, and the improvement of KPFCM-GWO is mainly attributed to the combination of the Gaussian kernel function and the parameter optimization capability of the GWO. What is more, the paper applies KPFCM-GWO to analyzes the value of an airline’s customers, and five levels of customer categories are defined.

With the development of the Internet and big data, more and more consumer behavior data are used in different forecasting problems, which greatly improve the performance of prediction. As the main travel tool, the sales of automobiles will change with the variations of the market and the external environment. Accurate prediction of automobile sales can not only help the dealers adjust their marketing plans dynamically but can also help the economy and the transportation sector make policy decisions. The automobile is a product with high value and high involvement, and its purchase decision can be affected by its own attributes, economy, policy and other factors. Furthermore, the sample data have the characteristics of various sources, great complexity and large volatility. Therefore, this paper uses the Support Vector Regression (SVR) model, which has global optimization, a simple structure, and strong generalization abilities and is suitable for multi-dimensional, small sample data to predict the monthly sales of automobiles. In addition, the parameters are optimized by the Grey Wolf Optimizer (GWO) algorithm to improve the prediction accuracy. First, the grey correlation analysis method is used to analyze and determine the factors that affect automobile sales. Second, it is used to build the GWO-SVR automobile sales prediction model. Third, the experimental analysis is carried out by using the data from Suteng and Kaluola in the Chinese car segment, and the proposed model is compared with the other four commonly used methods. The results show that the GWO-SVR model has the best performance of mean absolute percentage error (MAPE) and root mean square error (RMSE). Finally, some management implications are put forward for reference.

ABSTRACT: In order to make a breakthrough in Mesozoic-Paleozoic shale gas exploration in the South Yellow Sea Basin, a comparison of the preservation conditions was made within the Barnett shale gas reservoirs in the Fortworth Basin, the Jiaoshiba shale gas reservoirs in Sichuan Basin and potential shale gas reservoirs in Guizhou Province. The results show that the “Sandwich” structure is of great importance for shale gas accumulation. Therein to, the “Sandwich” structure is a kind of special reservoir-cap rock assemblage which consist of limestone or dolomite on the top, mudstone or shale layer in the middle and limestone or dolomite at the bottom. In consideration of the Mesozoic-Paleozoic in the Lower Yangtze, and Laoshan Uplift with weak Paleozoic deformation and thrust fault sealing on both flanks of the Laoshan Uplift, a conclusion can be drawn that the preservation conditions of shale gas probably developed “Sandwich” structures in the Lower Cambrian and Permian, which are key layers for the breakthrough of shale gas in the South Yellow Sea. Moreover, the preferred targets for shale gas drilling probably locate at both flanks of the Laoshan Uplift.

Ni nanocatalysts produced through exsolution have shown strong resistance to particle sintering and carbon coking in a beneficial dry reforming of methane (DRM) reaction utilizing greenhouse gases such as CH4 and CO2. However, most of the existing oxide supports for exsolution have been limited to perovskite oxide, while studies on fluorite support have been rarely conducted due to the limited solubility despite its excellent redox stability. Here we demonstrate that 3 ​mol% Ni can be successfully dissolved into the yttria-stabilized zirconia (YSZ) lattice and be further exsolved to the surface in a reducing atmosphere. The YSZ decorated with exsolved Ni nanoparticles shows enhanced catalytic activity for DRM reaction compared to the conventional cermet type of bulk Ni-YSZ. Moreover, the catalytic activity is extremely stable for about 300 ​h without significant degradation. Overall results suggest that the YSZ-based fluorite structure can be utilized as one of the support oxides for exsolution.

Sustainable supplier selection (SSS) is an important part of sustainable supply chain management (SSCM). In this paper, an integrated multi-criteria decision-making (MCDM) framework, based on the picture fuzzy exponential entropy, and the VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method, is proposed to manage SSS problems. Firstly, the evaluation criteria of SSS, including economic, environmental and social, is established. This can be evaluated in the form of the actual data or linguistic terms provided by suppliers and experts respectively in an actual decision-making process. Then, according to the translated scales, all the evaluation information can be converted into picture fuzzy numbers (PFNs). Secondly, the picture fuzzy exponential entropy is defined. Moreover, based on the entropy’s minimization principle, the defined picture fuzzy exponential entropy is used to determine the weight of the SSS’s criteria. Thirdly, the extended VIKOR method, which combines the grey correlation coefficient, is utilized to select a suitable supplier. This method avoids the shortcomings of the traditional VIKOR method in data mining and solves the conflict between SSS criteria. Finally, the feasibility and effectiveness of the proposed integrated decision framework are verified by an experiment, as well as a sensitivity analysis and comparative analysis. First published online 28 February 2020

A circular economy (CE) is proposed to mitigate resource shortage and environmental pollution. Given the inevitable conflict between the new development mode and traditional economic benefits, practical experience shows that CE implementation needs the support of outside forces, such as government policy interventions or environmental organisations’ propaganda guidance. On the basis of existing studies, the present work establishes a systematic economic model in accordance with the characteristics, objectives and principles of CE. The equilibrium solution and critical condition of government and non-government participation models are obtained through game analysis. We select the industrial waste water circulation of some provinces in China to illustrate the feasibility and effectiveness of the proposed model. Overall, our results indicate that the gap between the critical price and unit processing cost determines the promotion of CE and government intervention. Moreover, government intervention is critical to building a waste management department in its early stage. First published online 27 November 2019

With the development of the social economy and an enlarged volume of information, the application of multiple-criteria decision making (MCDM) has become increasingly wide and deep. As a brilliant MCDM technique, the best–worst method (BWM) has attracted many scholars’ attention because it can determine the weights of criteria with less comparison time and higher consistency between judgments than analytic hierarchy process. However, the effectiveness of the BWM is based on complete comparison information among criteria. Considering the fact that the decision makers may have limited time and energy to study all criteria, they cannot construct a complete comparison system. In this paper, we propose a novel MCDM method named BW-MaxEnt that combines BWM and the maximum entropy method (MaxEnt) to identify the weights of unfamiliar criteria with incomplete decision information. The model can be translated into a convex optimization problem that can be solved effectively and has an overall optimal solution. Finally, a practical application concerning the procurement of GPU workstations illustrates the feasibility of the proposed BW-MaxEnt method.